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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Related Experiment Video

Updated: May 20, 2025

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
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Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

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A bayesian approach to inclusion based rock physics modeling with multiple statistical ensembles.

Kyle T Spikes1, Mrinal K Sen2,3

  • 1Department of Earth and Planetary Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA. kyle.spikes@jsg.utexas.edu.

Scientific Reports
|March 26, 2025
PubMed
Summary

This study introduces a Bayesian approach to identify the most probable rock-physics models and their parameters. The method, applied to carbonate rock data, highlights the differential effective medium model as superior.

Keywords:
Bayesian analysisInformed distributionsPosterior combinationsRock physics

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Area of Science:

  • Geophysics
  • Petrophysics
  • Computational Science

Background:

  • Rock physics modeling often involves statistical simulations to fit data and quantify uncertainty.
  • Characterizing uncertainty in rock physics models is crucial for accurate subsurface interpretation.

Purpose of the Study:

  • To present a Bayesian approach for determining the most probable rock-physics models.
  • To identify probable combinations of model inputs using posterior distributions.
  • To assess uncertainty in rock physics modeling.

Main Methods:

  • Employed a Bayesian framework for statistical analysis of rock physics models.
  • Utilized exhaustive sampling to calculate full posterior distributions for ensembles of model inputs.
  • Applied the method to two inclusion-based models: self-consistent and differential effective medium.

Main Results:

  • The differential effective medium model was identified as the most probable for the studied carbonate rock dataset.
  • Analysis revealed distinct clusters within the input parameters of the most probable models.
  • The Bayesian approach effectively characterizes uncertainty and identifies key model inputs.

Conclusions:

  • The presented Bayesian method offers a robust framework for rock physics model selection and uncertainty quantification.
  • The differential effective medium model is a strong candidate for datasets with similar velocity-porosity trends.
  • This computational approach is broadly applicable to various datasets and models, though it requires parallel computation.